{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/johnbh/Dropbox/Work/Democracy of Dating/Data/nicholson replication/nicholson estimates.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}31 Mar 2019, 08:15:32
{txt}
{com}. svy: regress attractrate_std same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       570
{txt}{col 1}Number of PSUs{col 20}= {res}      570{txt}{col 49}Population size{col 67}={res} 525.209022
{txt}{col 49}Design df{col 67}= {res}       569
{txt}{col 49}F({res}   1{txt},{res}    569{txt}){col 67}= {res}     71.68
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2111

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}attractrat~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} 1.053255{col 26}{space 2} .1244019{col 37}{space 1}    8.47{col 46}{space 3}0.000{col 54}{space 4} .8089121{col 67}{space 3} 1.297598
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.5907221{col 26}{space 2} .1086192{col 37}{space 1}   -5.44{col 46}{space 3}0.000{col 54}{space 4}-.8040656{col 67}{space 3}-.3773786
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. 1.053255 /0.757
{bf}{err}1.053255{sf} is not a valid command name
{txt}{search r(199), local:r(199);}

{com}. disp 1.053255 /0.757
{res}1.391354

{com}. do "/var/folders/c1/0d85h4zd4hs58m9b1fbp0dgh0000gp/T//SD04057.000000"
{txt}
{com}. *CCES Data
. 
. cd "/Users/johnbh/Dropbox/Work/Democracy of Dating/Data/cces/BYU module data/" // change this if you're working on another computer
{res}/Users/johnbh/Dropbox/Work/Democracy of Dating/Data/cces/BYU module data
{txt}
{com}. 
. use "CCES18_BYU_OUTPUT.DTA", clear
{txt}( )

{com}. 
. *recode the variables for easier usage
. gen attractiveness_score=.
{txt}(1,000 missing values generated)

{com}. replace attractiveness_score=BYU307 if BYU307<=100
{txt}(992 real changes made)

{com}. 
. gen respond_message=.
{txt}(1,000 missing values generated)

{com}. replace respond_message=BYU308_1 if BYU308_1<=6
{txt}(996 real changes made)

{com}. 
. recode respond_message (6=1) (5=2) (4=3) (3=4) (2=5) (1=6), gen(respond_message_flip)
{txt}(996 differences between respond_message and respond_message_flip)

{com}. 
. gen go_date=.
{txt}(1,000 missing values generated)

{com}. replace go_date=BYU308_2 if BYU308_2<=6
{txt}(992 real changes made)

{com}. 
. recode go_date (6=1) (5=2) (4=3) (3=4) (2=5) (1=6), gen(go_date_flip)
{txt}(992 differences between go_date and go_date_flip)

{com}. 
. 
. gen relationship=.
{txt}(1,000 missing values generated)

{com}. replace relationship=BYU308_3 if BYU308_3<=6
{txt}(994 real changes made)

{com}. 
. recode relationship (6=1) (5=2) (4=3) (3=4) (2=5) (1=6), gen(relationship_flip)
{txt}(994 differences between relationship and relationship_flip)

{com}. 
. 
. *Standardize our outcomes
. egen attractiveness_score_std=std(attractiveness_score), mean(0) std(1)
{txt}(8 missing values generated)

{com}. egen respond_message_flip_std=std(respond_message_flip), mean(0) std(1) 
{txt}(4 missing values generated)

{com}. egen relationship_flip_std=std(relationship_flip), mean(0) std(1) 
{txt}(6 missing values generated)

{com}. egen go_date_flip_std=std(go_date_flip), mean(0) std(1) 
{txt}(8 missing values generated)

{com}. 
. *BYU306 -> 1=Rep, 2=Dem, 3=Ind (Respondent party)
. 
. *BYU305 -> 1=Men, 2=Women (This is preference)
. 
. 
. ******************************************************************************
. ******************************************************************************
. *Begin here if you use the updated .csv
. 
. tab BYU307rand

 {txt}BYU307 randomization {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
           Republican {c |}{res}        338       33.80       33.80
{txt}             Democrat {c |}{res}        332       33.20       67.00
{txt}No politics mentioned {c |}{res}        330       33.00      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      1,000      100.00
{txt}
{com}. *Which one is 1, 2, 3?
. *1 Republican
. *2 Democrat
. *3 Control
. 
. gen same_party=.
{txt}(1,000 missing values generated)

{com}. replace same_party=1 if BYU306==1 & BYU307rand==3
{txt}(107 real changes made)

{com}. replace same_party=1 if BYU306==2 & BYU307rand==1
{txt}(117 real changes made)

{com}. replace same_party=0 if BYU306==1 & BYU307rand==1
{txt}(102 real changes made)

{com}. replace same_party=0 if BYU306==2 & BYU307rand==3
{txt}(134 real changes made)

{com}. 
. svyset [pw=teamweight]

      {txt}pweight:{col 16}{res}teamweight
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}<observations>
        FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. svy: reg attractiveness_score same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       457
{txt}{col 1}Number of PSUs{col 20}= {res}      457{txt}{col 49}Population size{col 67}={res} 446.907765
{txt}{col 49}Design df{col 67}= {res}       456
{txt}{col 49}F({res}   1{txt},{res}    456{txt}){col 67}= {res}     48.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1306

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}attractive~e{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} 19.49672{col 26}{space 2} 2.788418{col 37}{space 1}    6.99{col 46}{space 3}0.000{col 54}{space 4} 14.01698{col 67}{space 3} 24.97647
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 51.97057{col 26}{space 2} 2.169157{col 37}{space 1}   23.96{col 46}{space 3}0.000{col 54}{space 4} 47.70779{col 67}{space 3} 56.23336
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. svy: reg attractiveness_score_std same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       457
{txt}{col 1}Number of PSUs{col 20}= {res}      457{txt}{col 49}Population size{col 67}={res} 446.907765
{txt}{col 49}Design df{col 67}= {res}       456
{txt}{col 49}F({res}   1{txt},{res}    456{txt}){col 67}= {res}     48.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1306

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}attractive~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} .7575877{col 26}{space 2}   .10835{col 37}{space 1}    6.99{col 46}{space 3}0.000{col 54}{space 4} .5446604{col 67}{space 3}  .970515
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4395793{col 26}{space 2} .0842873{col 37}{space 1}   -5.22{col 46}{space 3}0.000{col 54}{space 4}-.6052191{col 67}{space 3}-.2739396
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy: reg respond_message_flip_std same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       457
{txt}{col 1}Number of PSUs{col 20}= {res}      457{txt}{col 49}Population size{col 67}={res} 448.024856
{txt}{col 49}Design df{col 67}= {res}       456
{txt}{col 49}F({res}   1{txt},{res}    456{txt}){col 67}= {res}     14.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0001
{txt}{col 49}R-squared{col 67}= {res}    0.0458

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}respond_me~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} .4561539{col 26}{space 2} .1184598{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .2233591{col 67}{space 3} .6889488
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2072144{col 26}{space 2} .0854596{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.3751579{col 67}{space 3} -.039271
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy: reg go_date_flip_std same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       456
{txt}{col 1}Number of PSUs{col 20}= {res}      456{txt}{col 49}Population size{col 67}={res} 446.656805
{txt}{col 49}Design df{col 67}= {res}       455
{txt}{col 49}F({res}   1{txt},{res}    455{txt}){col 67}= {res}     18.99
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0558

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}go_date_fl~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} .5039818{col 26}{space 2} .1156392{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4} .2767286{col 67}{space 3} .7312349
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2197648{col 26}{space 2} .0789686{col 37}{space 1}   -2.78{col 46}{space 3}0.006{col 54}{space 4}-.3749531{col 67}{space 3}-.0645765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. svy: reg relationship_flip_std same_party
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       457
{txt}{col 1}Number of PSUs{col 20}= {res}      457{txt}{col 49}Population size{col 67}={res} 448.024856
{txt}{col 49}Design df{col 67}= {res}       456
{txt}{col 49}F({res}   1{txt},{res}    456{txt}){col 67}= {res}     18.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0561

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}relationsh~d{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}same_party {c |}{col 14}{res}{space 2} .5040718{col 26}{space 2} .1159978{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} .2761151{col 67}{space 3} .7320284
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2280069{col 26}{space 2} .0849441{col 37}{space 1}   -2.68{col 46}{space 3}0.008{col 54}{space 4}-.3949372{col 67}{space 3}-.0610765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. 